Major role for mRNA stability in shaping the kinetics of gene induction.

Elkon R, Zlotorynski E, Zeller KI, Agami R - BMC Genomics (2010)

Bottom Line:
In particular, kinetic waves in transcriptional responses are usually interpreted as resulting from sequential activation of transcription factors.Such effect cannot be explained even by a complete shut-off of transcription, and therefore indicates intense modulation of RNA stability.Taken together, our results demonstrate the key role of mRNA stability in determining induction kinetics in mammalian transcriptional networks.

Background: mRNA levels in cells are determined by the relative rates of RNA production and degradation. Yet, to date, most analyses of gene expression profiles were focused on mechanisms which regulate transcription, while the role of mRNA stability in modulating transcriptional networks was to a large extent overlooked. In particular, kinetic waves in transcriptional responses are usually interpreted as resulting from sequential activation of transcription factors.

Results: In this study, we examined on a global scale the role of mRNA stability in shaping the kinetics of gene response. Analyzing numerous expression datasets we revealed a striking global anti-correlation between rapidity of induction and mRNA stability, fitting the prediction of a kinetic mathematical model. In contrast, the relationship between kinetics and stability was less significant when gene suppression was analyzed. Frequently, mRNAs that are stable under standard conditions were very rapidly down-regulated following stimulation. Such effect cannot be explained even by a complete shut-off of transcription, and therefore indicates intense modulation of RNA stability.

Conclusion: Taken together, our results demonstrate the key role of mRNA stability in determining induction kinetics in mammalian transcriptional networks.

Figure 1: Kinetics of gene induction. (A) Two mechanisms which underlie the kinetics of gene induction are sequential activation of TFs and mRNA stability. While much research attention was given to the former, the latter was overlooked by many studies. Both mechanisms act in cells in parallel, and thus, the observed dynamics of transcriptional response reflects their superposition. (In the cartoon, waves 1, 2 and 3 refer to early-, intermediate- and late- kinetic responses.) (B) The standard kinetic model predicts that the rapidity of a transition between former and new transcript steady states is determined by the transcript's stability (T1/2 = ln2/α). The figure shows simulated kinetic response of four mRNAs with the same transcription rate (β = 5) and different degradation rates (blue: α = 2.0; green: α = 1.0; red: α = 0.5; black: α = 0.2). A pulse stimulation was exerted at t = 0 and terminated at t = 5. Note that upon induction, the most unstable mRNA (blue, highest α) reaches the lowest steady-state level, but it does so very rapidly (lowest T1/2). (C) Transcription rates of the four mRNAs were adjusted to bring them to the same level at t = 5.

Mentions:
Time-course analysis is a very common design for microarray analysis, which allows researchers to follow the dynamics of the cellular response to perturbations. Clustering analysis applied to time-course data partitions the response into distinct kinetic waves, distinguishing between early-, intermediate- and late- responding genes. Such kinetic waves can be the result of sequential activation of primary and secondary transcription factors (TFs) [8,9]. Nevertheless, mathematical modeling of changes in RNA levels predicts that mRNA degradation rate plays a pivotal role in shaping the kinetics of genes' response [10,11]. A standard measure for the speed of a transition between two steady states is T1/2, which is the time at which half of the change between the new and the former steady-state levels is achieved. (In the phase of mRNA decay T1/2 is usually referred to as 'T half-life' which measures the period of time it takes for a transcript undergoing decay to decrease its level by half). Importantly, a simple kinetic model predicts that T1/2 is determined by RNA degradation rate not only in decay of expression but also in the phase of induction. The model assumes that mRNA is produced at a constant rate (β) while the rate of its degradation is proportional to its concentration. Accordingly, the rate of change in mRNA concentration (X) is given by the equation: dX/dt = β-αX (α denotes the degradation rate constant). At steady state mRNA concentration reaches equilibrium (that is, there is no change: dX/dt = 0), and therefore, the steady state level (Xss) is determined by the ratio between the synthesis and degradation constants: Xss = β/α. Solving the above equation gives the change in mRNA concentration over time: ΔX(t) = [β/α-X0]*(1-e-αt) (X0 represents the mRNA concentration at t0 where the perturbation was applied to the system, assuming that change in transcription rate occurs instantaneously). Of note, according to this solution, the rapidity of a transition between former and new steady states (T1/2) is determined only by α, and is inversely proportional to it: T1/2 = ln2/α; Therefore, genes with unstable RNA are predicted to respond in fast kinetics, whereas genes with stable RNA should respond more slowly (Figure 1; For a thorough discussion of the kinetic model see [10]). Furthermore, this model predicts that the time it takes a transcript whose transcription rate is increased by a factor L to achieve a k-fold induction in expression is a linear function of T1/2: Tk = -log2(1-f)*T1/2; where f = (k-1)/(L-1) [12]; That is, for a similar increase in transcription rate in response to a stimulus, genes encoding unstable mRNAs are predicted to achieve a certain fold of induction faster than genes which encode stable transcripts.

Figure 1: Kinetics of gene induction. (A) Two mechanisms which underlie the kinetics of gene induction are sequential activation of TFs and mRNA stability. While much research attention was given to the former, the latter was overlooked by many studies. Both mechanisms act in cells in parallel, and thus, the observed dynamics of transcriptional response reflects their superposition. (In the cartoon, waves 1, 2 and 3 refer to early-, intermediate- and late- kinetic responses.) (B) The standard kinetic model predicts that the rapidity of a transition between former and new transcript steady states is determined by the transcript's stability (T1/2 = ln2/α). The figure shows simulated kinetic response of four mRNAs with the same transcription rate (β = 5) and different degradation rates (blue: α = 2.0; green: α = 1.0; red: α = 0.5; black: α = 0.2). A pulse stimulation was exerted at t = 0 and terminated at t = 5. Note that upon induction, the most unstable mRNA (blue, highest α) reaches the lowest steady-state level, but it does so very rapidly (lowest T1/2). (C) Transcription rates of the four mRNAs were adjusted to bring them to the same level at t = 5.

Mentions:
Time-course analysis is a very common design for microarray analysis, which allows researchers to follow the dynamics of the cellular response to perturbations. Clustering analysis applied to time-course data partitions the response into distinct kinetic waves, distinguishing between early-, intermediate- and late- responding genes. Such kinetic waves can be the result of sequential activation of primary and secondary transcription factors (TFs) [8,9]. Nevertheless, mathematical modeling of changes in RNA levels predicts that mRNA degradation rate plays a pivotal role in shaping the kinetics of genes' response [10,11]. A standard measure for the speed of a transition between two steady states is T1/2, which is the time at which half of the change between the new and the former steady-state levels is achieved. (In the phase of mRNA decay T1/2 is usually referred to as 'T half-life' which measures the period of time it takes for a transcript undergoing decay to decrease its level by half). Importantly, a simple kinetic model predicts that T1/2 is determined by RNA degradation rate not only in decay of expression but also in the phase of induction. The model assumes that mRNA is produced at a constant rate (β) while the rate of its degradation is proportional to its concentration. Accordingly, the rate of change in mRNA concentration (X) is given by the equation: dX/dt = β-αX (α denotes the degradation rate constant). At steady state mRNA concentration reaches equilibrium (that is, there is no change: dX/dt = 0), and therefore, the steady state level (Xss) is determined by the ratio between the synthesis and degradation constants: Xss = β/α. Solving the above equation gives the change in mRNA concentration over time: ΔX(t) = [β/α-X0]*(1-e-αt) (X0 represents the mRNA concentration at t0 where the perturbation was applied to the system, assuming that change in transcription rate occurs instantaneously). Of note, according to this solution, the rapidity of a transition between former and new steady states (T1/2) is determined only by α, and is inversely proportional to it: T1/2 = ln2/α; Therefore, genes with unstable RNA are predicted to respond in fast kinetics, whereas genes with stable RNA should respond more slowly (Figure 1; For a thorough discussion of the kinetic model see [10]). Furthermore, this model predicts that the time it takes a transcript whose transcription rate is increased by a factor L to achieve a k-fold induction in expression is a linear function of T1/2: Tk = -log2(1-f)*T1/2; where f = (k-1)/(L-1) [12]; That is, for a similar increase in transcription rate in response to a stimulus, genes encoding unstable mRNAs are predicted to achieve a certain fold of induction faster than genes which encode stable transcripts.

Bottom Line:
In particular, kinetic waves in transcriptional responses are usually interpreted as resulting from sequential activation of transcription factors.Such effect cannot be explained even by a complete shut-off of transcription, and therefore indicates intense modulation of RNA stability.Taken together, our results demonstrate the key role of mRNA stability in determining induction kinetics in mammalian transcriptional networks.

Background: mRNA levels in cells are determined by the relative rates of RNA production and degradation. Yet, to date, most analyses of gene expression profiles were focused on mechanisms which regulate transcription, while the role of mRNA stability in modulating transcriptional networks was to a large extent overlooked. In particular, kinetic waves in transcriptional responses are usually interpreted as resulting from sequential activation of transcription factors.

Results: In this study, we examined on a global scale the role of mRNA stability in shaping the kinetics of gene response. Analyzing numerous expression datasets we revealed a striking global anti-correlation between rapidity of induction and mRNA stability, fitting the prediction of a kinetic mathematical model. In contrast, the relationship between kinetics and stability was less significant when gene suppression was analyzed. Frequently, mRNAs that are stable under standard conditions were very rapidly down-regulated following stimulation. Such effect cannot be explained even by a complete shut-off of transcription, and therefore indicates intense modulation of RNA stability.

Conclusion: Taken together, our results demonstrate the key role of mRNA stability in determining induction kinetics in mammalian transcriptional networks.